SunGard: Non-Maturing Deposits — ALM Modelling

Posted: 1 December 2013

Non-Maturing Deposits (NMDs) is a term commonly used to describe those deposits which do not have a contractually definite maturity date, and are typically withdrawable on demand by the customers. Savings accounts, current accounts, and checking accounts are some of the typical examples of NMDs. Such accounts make up a sizeable part (up to 50% in the case of retail banks) of the liability side of the balance sheet of deposit taking institutions. Hence, it is critical to get the analysis and modelling for this portion of the balance sheet correct.

NMDs need a special treatment when generating their cash flow and interest rate risk profiles. NMDs lack both predetermined maturity and rate reset dates. The depositor is free to withdraw funds at any time and that the borrowing institution is free to change the rate at any point (except in certain regulated markets where such rates are regulatory driven). Most Asset-Liability (A/L) managers would accept that modelling NMDs is one of the most critical challenges faced when assessing overall balance sheet risk.

NMDs are not new products. The modelling considerations for them are not a recent development – but something which has always troubled many, for too long. Over the years, there may be many analytical and statistical techniques which have been employed by various institutions to address the problem. However, to date there has been no particular method or technique which has emerged as the de-facto practice. Most practices have their own advantages and disadvantages and bring in a fresh set of challenges. Also, since these products are not market traded (banking book products), there are no market-value based comparison benchmarks for institutions to have a common framework to treat NMDs.

This paper looks to reinforce the criticality of NMDs modelling for the purpose of Asset Liability Management (ALM) and related Balance Sheet Management practices. The paper discusses the most popular modelling alternatives and makes a case for some common best practices that should be considered when modelling the behavior of such products.